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洞見 - Machine Learning Interatomic Potentials
Cartesian Atomic Cluster Expansion for Machine Learning Interatomic Potentials
Proposing a Cartesian-based atomic density expansion as an alternative to spherical harmonics for accurate and efficient interatomic potentials.
A Review of Machine Learning Interatomic Potentials: From GAP to ACE to MACE
This commentary reviews the development and advancements of machine learning interatomic potentials (MLIPs), highlighting the evolution from Gaussian Approximation Potentials (GAP) to Atomic Cluster Expansion (ACE) and its nonlinear extension, Multi-layer ACE (MACE).
Enhancing Machine Learning Interatomic Potentials with Polarizable Long-Range Interactions for Accurate Materials Simulation
Incorporating explicit polarizable long-range interactions into machine learning interatomic potentials significantly enhances their accuracy in predicting material properties and simulating complex phenomena, offering a computationally efficient alternative to ab initio methods.
